Scanning transmission electron microscope having multiple beams and post-detection image correction

ABSTRACT

Embodiments are further directed to an information processing system for generating a corrected image of a sample. The system includes a detector, a memory communicatively coupled to the detector, and a post-detection image processor communicatively coupled to the memory and the detector. The system is configured to perform a method that includes detecting, by the detector, data of a plurality of moving particles, wherein the data of the plurality of moving particles correspond to an uncorrected image of the sample, and wherein the uncorrected image includes defocus, astigmatism and spherical aberration. The method further includes generating, by the post-detection image processor, a corrected image of the sample based at least in part on processing the detected data of the plurality of moving particles.

DOMESTIC PRIORITY

The present application claims priority to U.S. Non-provisionalapplication Ser. No. 14/683,679 filed on Apr. 10, 2015 titled “SCANNINGTRANSMISSION ELECTRON MICROSCOPE HAVING MULTIPLE BEAMS ANDPOST-DETECTION IMAGE CORRECTION,” assigned to the assignee hereof andexpressly incorporated by reference herein.

The present disclosure relates in general to the generation of highresolution images in microscopy. More specifically, the presentdisclosure relates to systems and methodologies for correcting errors inan image that has been detected by an electron microscope.

Unlike light microscopes, electron microscopes visualize objects using athin beam of rapidly moving electrons that interact with a sample.Because the wavelength of an electron can be up to 100,000 times shorterthan that of visible light photons, the electron microscope has a higherresolving power than a light microscope and can reveal the structure ofsmaller objects. For example, a transmission electron microscope canachieve better than 0.50 angstroms of resolution, as well asmagnifications of up to about 10,000,000 times. In contrast, a typicallight microscope is limited by diffraction to about 200 nanometers ofresolution and useful magnifications below 2000 times.

In a conventional electron microscope configuration, the electrons areemitted by an electron source. As the electrons move from the electronsource through a high vacuum chamber, the electrons are focused into abeam and maintained by a series of electromagnetic lenses. The electronsin the beam encounter the sample and are either absorbed by, scatteredby or passed through the sample. Because different regions of the sampleare variously transparent to electrons, different amounts of electronswith changed energies and/or phases pass through these regions.Electrons that have encountered the sample are collected by detectors,which in effect capture an image of the sample.

In order for existing microscopes to operate successfully, theillumination that forms the image must be carefully controlled. Forexisting electron microscopes, in addition to maintaining focus, theelectron beam typically must also be managed in order to correctastigmatism and spherical aberration errors that may be introduced priorto image capture and detection. Managing the beam to maintain focus andcorrect astigmatism and spherical aberration errors prior to imagedetection typically requires periodic human intervention to manuallyadjust the electron microscope, along with potentially expensiveadditional hardware and/or software functionality provided within themicroscope.

SUMMARY

Embodiments are directed to an information processing system forgenerating a corrected image of a sample. The system includes adetector, a memory communicatively coupled to the detector, and apost-detection image processor communicatively coupled to the memory andthe detector. The detector is configured to detect data of a pluralityof moving particles, wherein the data of the plurality of movingparticles correspond to an uncorrected image of the sample, and whereinthe uncorrected image includes defocus, astigmatism and sphericalaberration. The post-detection image processor is configured to generatea corrected image of the sample based at least in part on processing thedetected data of the plurality of moving particles.

Embodiments are further directed to an information processing system forgenerating a corrected image of a sample. The system includes adetector, a memory communicatively coupled to the detector, and apost-detection image processor communicatively coupled to the memory andthe detector. The system is configured to perform a method that includesdetecting, by the detector, data of a plurality of moving particles,wherein the data of the plurality of moving particles correspond to anuncorrected image of the sample, and wherein the uncorrected imageincludes defocus, astigmatism and spherical aberration. The methodfurther includes generating, by the post-detection image processor, acorrected image of the sample based at least in part on processing thedetected data of the plurality of moving particles.

Embodiments are further directed to a computer program product forgenerating a corrected image of a sample. The computer program productincludes a non-transitory computer readable storage medium havingprogram instructions embodied therewith, wherein the programinstructions are readable by at least one processor circuit to cause theat least one processor circuit to perform a method. The method includesreceiving data of a plurality of moving particles, wherein the data ofthe plurality of moving particles correspond to an uncorrected image ofthe sample, and wherein the uncorrected image of the sample includesdefocus, astigmatism and spherical aberration. The method furtherincludes generating a corrected image of the sample based at least inpart on processing the detected data of the plurality of movingparticles.

Additional features and advantages are realized through the techniquesdescribed herein. Other embodiments and aspects are described in detailherein. For a better understanding, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the present disclosure isparticularly pointed out and distinctly claimed in the claims at theconclusion of the specification. The foregoing and other features andadvantages are apparent from the following detailed description taken inconjunction with the accompanying drawings in which:

FIG. 1 depicts a cloud computing node according to one or moreembodiments;

FIG. 2 depicts a cloud computing environment according to one or moreembodiments;

FIG. 3 depicts abstraction model layers according to one or moreembodiments;

FIG. 4 depicts a simplified diagram illustrating a system according toone or more embodiments;

FIG. 5 depicts a simplified diagram illustrating a focused electron beamilluminating a transparent sample;

FIG. 6A depicts a simplified diagram illustrating a slightly defocusedbeam using a single integrating detector;

FIG. 6B depicts a simplified diagram illustrating a slightly defocusedbeam using a position sensitive detector;

FIG. 7A depicts a simplified diagram illustrating the focusing of anelectron beam without spherical aberration;

FIG. 7B depicts a simplified diagram illustrating the focusing of anelectron beam with spherical aberration;

FIG. 8 depicts a simplified diagram illustrating a slightly defocusedbeam using a single integrating detector, wherein a spatial electrondistribution (i.e., virtual detector) is positioned between the sampleand the single integrating detector according to one or moreembodiments;

FIG. 9 depicts a simplified diagram illustrating a multiple electronbeam configuration according to one or more embodiments;

FIG. 10 depicts equations that may be used in correcting an image fordefocus, astigmatism and spherical aberration according to one or moreembodiments;

FIG. 11 depicts a flow diagram illustrating a methodology according toone or more embodiments; and

FIG. 12 depicts a computer program product in accordance with one ormore embodiments.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with three or four digit reference numbers. The leftmostdigit(s) of each reference number corresponds to the figure in which itselement is first illustrated.

DETAILED DESCRIPTION

Various embodiments of the present disclosure will now be described withreference to the related drawings. Alternate embodiments may be devisedwithout departing from the scope of this disclosure. It is noted thatvarious connections are set forth between elements in the followingdescription and in the drawings. These connections, unless specifiedotherwise, may be direct or indirect, and the present disclosure is notintended to be limiting in this respect. Accordingly, a coupling ofentities may refer to either a direct or an indirect connection.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any networked or other type of computing environmentnow known or later developed. Additionally, although the presentdisclosure includes embodiments directed to transmission electronmicroscopy, implementation of the teachings recited herein may apply toother types of microscopy as well, including light based microscopy aslong as there is a need to correct image resolution defects includingbut not limited to defocus, astigmatism and/or spherical aberration.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows: Software as a Service (SaaS): thecapability provided to the consumer is to use the provider'sapplications running on a cloud infrastructure. The applications areaccessible from various client devices through a thin client interfacesuch as a web browser (e.g., web-based e-mail). The consumer does notmanage or control the underlying cloud infrastructure including network,servers, operating systems, storage, or even individual applicationcapabilities, with the possible exception of limited user-specificapplication configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,electron microscope system 54D, and/or automobile computer system 54Nmay communicate. Nodes 10 may communicate with one another. They may begrouped (not shown) physically or virtually, in one or more networks,such as Private, Community, Public, or Hybrid clouds as describedhereinabove, or a combination thereof. This allows cloud computingenvironment 50 to offer infrastructure, platforms and/or software asservices for which a cloud consumer does not need to maintain resourceson a local computing device. It is understood that the types ofcomputing devices 54A-N shown in FIG. 2 are intended to be illustrativeonly and that computing nodes 10 and cloud computing environment 50 cancommunicate with any type of computerized device over any type ofnetwork and/or network addressable connection (e.g., using a webbrowser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and a module 96 for supporting post-detectionimage correction according to one or more embodiments of the presentdisclosure.

Turning now to a more detailed description of the background technologyof the present disclosure, as previously noted herein, an electronmicroscope is a microscope that uses accelerated electrons as a sourceof illumination. Because the wavelength of an electron can be up to100,000 times shorter than that of visible light photons, the electronmicroscope has a higher resolving power than a light microscope and canreveal the structure of smaller objects. For example, a transmissionelectron microscope can achieve better than 0.50 angstroms ofresolution, as well as magnifications of up to about 10,000,000 times.In contrast, a typical light microscope is limited by diffraction toabout 200 nanometers of resolution and useful magnifications below 2000times. A typical configuration of an electron microscope includeselectrostatic and/or electromagnetic lenses to control the electron beamand focus it to form an image. Modern electron microscopes produceelectron micrographs, using specialized digital cameras or framegrabbers to capture the image. The electron optical lenses are analogousto the glass lenses of an optical light microscope. Electron microscopesare used to investigate the ultra-structure of a wide range ofbiological and inorganic specimens including microorganisms, cells,large molecules, biopsy samples, metals and crystals. Industrially,electron microscopes are often used for quality control and failureanalysis.

The original form of electron microscope, the transmission electronmicroscope (TEM) uses a high voltage electron beam to create an image.The electron beam is produced by an electron gun, often fitted with atungsten filament cathode as the electron source. The electron beam isaccelerated by an anode typically at +100 keV (40 to 400 keV) withrespect to the cathode, focused by electrostatic and electromagneticlenses, and transmitted through the specimen that is in part transparentto electrons and in part scatters them out of the beam. When it emergesfrom the specimen, the electron beam carries information about thestructure of the specimen that is magnified by the objective lens systemof the microscope. The spatial variation in this information (the“image”) may be viewed by projecting the magnified electron image onto afluorescent viewing screen coated with a phosphor or scintillatormaterial such as zinc sulfide. Alternatively, the image can bephotographically recorded by exposing a photographic film or platedirectly to the electron beam, or a high-resolution phosphor may becoupled by means of a lens optical system or a fiber optic light-guideto the sensor of a CCD (charge-coupled device) camera, or the image maybe recorded by direct detection of the electrons by a CCD or CMOS camerapositioned inside the electron microscope vacuum system. The imagedetected by the CCD or CMOS camera may be displayed on a monitor orcomputer.

A scanning electron microscope (SEM) is a type of electron microscopethat produces images of a sample by scanning it with a focused beam ofelectrons. The electrons interact with atoms in the sample, producingvarious signals that can be detected and that contain information aboutthe sample's surface topography and composition. The electron beam isgenerally scanned in a raster scan pattern, and the beam's position iscombined with the detected signal to produce an image. SEM can achieveresolution better than 1 nanometer. Specimens can be observed in highvacuum, in low vacuum, in dry conditions (in environmental SEM), and ata wide range of cryogenic or elevated temperatures.

A common mode of detection in an SEM is by secondary electrons emittedby atoms excited by the electron beam. By scanning the sample anddetecting the secondary electrons, an image displaying the topography orother spatial variations of the surface (such as compositionalvariations) is created. The resolution is limited by the microscope'sability to bring the electron beam to a fine focus, which may be limitedby defocus, astigmatism, and spherical aberration, as well as mechanicaland electronic instabilities in the microscope system.

A scanning transmission electron microscope (STEM) combines theoperating principles of TEMs and SEMs. As with any transmissionillumination scheme, the electrons pass through a sufficiently thinspecimen. However, STEM is distinguished from conventional TEM byfocusing the electron beam into a narrow spot which is scanned over thesample in a raster. The raster scanning of the beam across the samplemakes these microscopes suitable for analysis techniques such as mappingby energy dispersive X-ray (EDX) spectroscopy, electron energy lossspectroscopy (EELS) and annular dark-field imaging (ADF). These signalscan be obtained simultaneously, allowing direct correlation of image andquantitative spectroscopic data.

By using a STEM and a high-angle detector, it is possible to form atomicresolution images where the contrast is directly related to the atomicnumber (z-contrast image). The directly interpretable z-contrast imageis an appealing feature of STEM imaging with a high-angle detector. Thisis in contrast to the conventional high resolution electron microscopytechnique, which uses phase-contrast, and therefore often producesresults which require interpretation by comparison with imagesimulations. Usually a STEM is a conventional transmission electronmicroscope equipped with additional scanning coils, detectors and neededcircuitry. However, dedicated STEMs are also manufactured.

In order for any microscope to successfully capture a high resolutionimage, the image forming system, comprising illumination optics,objective lens, and post-specimen lenses including projection lenses, aswell as steering and correction optics, must be carefully aligned,adjusted, and focused. For known electron microscopes, in addition tomaintaining focus, capturing a high resolution image also requires thatthe electron beam is controlled throughout multiple iterations tocorrect astigmatism and spherical aberration errors that may beintroduced prior to image detection and capture. Controlling the beam tomaintain focus and correct astigmatism and spherical aberration errorsprior to image detection typically requires periodic human interventionto adjust the electron microscope, along with potentially expensiveadditional hardware and/or software functionality provided within themicroscope itself.

The present disclosure relates in general to the generation of highresolution images in electron microscopy. As used in the presentdisclosure, a high resolution image is an image wherein selected defects(e.g., defocus, astigmatism or spherical aberration) are not present. Inthe present disclosure, the terms uncorrected image refer to an imagefor which the selected errors are present, and the terms corrected imagerefer to an image for which the selected errors are not present. Thepresent disclosure further relates to systems and methodologies forcorrecting selected errors in an image obtained through electronmicroscopy. The selected errors that may be present in the detected,uncorrected image include but are not limited to defocus, astigmatismand spherical aberration. According to one or more embodiments, afocused electron beam illuminates a small spot on a sample. The spot maybe one pixel in the final image. The image is acquired one pixel at atime by scanning either the electron beam or the sample. In known STEMinstruments the signal used to construct the image consists of all orsome selected part of the electrons transmitted by the sample. Thedetector integrates over some range of azimuth and polar angle. In thesystem disclosed herein, the electron beam is focused in a plane that isclose to the image plane, but defocused from the exact image plane suchthat the observed image contains both real-space and angular-spaceinformation. By acquiring real-space and angular-space information foreach image pixel, the uncorrected image can be corrected, post-detection(i.e., after data collection has been completed), for defocus,astigmatism, and spherical aberration. Significantly, the final imageresolution can be better than the profile of the illuminating electronbeam, broadened by defocus, astigmatism, and spherical aberration.

In one or more embodiments, the post-detection correction systems andmethodologies of the present disclosure may be applied to an array ofelectron beams incident on the sample. For each electron beam thedetector acquires an “N” by “M” (N×M) image, which is a matrix with Nrows and M columns. For each pixel in this N×M matrix, the imageintensity is measured. Each electron beam will suffer from errors,including but not limited to defocus, astigmatism, and sphericalaberration, thereby collecting an uncorrected image on the detector foreach electron beam. Using the post-detection correction systems andmethodologies of the present disclosure, the uncorrected image obtainedwith each electron beam is corrected to obtain an image corrected fordefocus, astigmatism and spherical aberration. The final correctedimage, constructed from all the uncorrected images obtained with theelectron beam array, is assembled from the corrected images obtained foreach electron beam. It is noted that, although the present disclosureincludes embodiments directed to transmission electron microscopy,implementation of the teachings recited herein may apply to other typesof microscopy as well, including light based microscopy as long as thereis a need to correct image resolution defects including but not limitedto defocus, astigmatism and/or spherical aberration.

Turning now to the drawings in greater detail, wherein like referencenumerals indicate like elements, FIG. 4 depicts a diagram illustrating asystem 400 according to one or more embodiments. System 400 includes amicroscope 402, a post-detection processor 430, a corrected image 432and cloud 50 (also shown in FIG. 2), configured and arranged as shown.Microscope 402 may be implemented as a high vacuum chamber electronmicroscope that includes an electron source 404, electrons 406, a firstset of electrostatic and/or electromagnetic focusing lenses 408,electron beam(s) 410, a sample 412, a second set of electrostatic and/orelectromagnetic focusing lenses 414 and a detector 416, configured andarranged as shown. Detector 416 is configured to detect the movingparticles that form electron beam(s) 410 and generate data of the movingparticles. An uncorrected image 418 may be derived from data of themoving particles. Electron microscope 402 may operate in a variety ofmodes, including but not limited to TEM, SEM or STEM modes. Cloud 50 maysupplement, support or replace some or all of the functionality ofpost-detection image processor 430 and corrected image 432.Additionally, some or all of the functionality of post-detection imageprocessor 430 and corrected image 432 may be implemented as a node 10(shown in FIGS. 1 and 2) of cloud 50. Cloud 50 is one example of anetworked computing environment that may be used to implement one ormore embodiments of the present disclosure. As previously noted herein,it is understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any networked or other type of computing environmentnow known or later developed. Additionally, although source 404generates electrons, for configurations wherein the teachings of thepresent disclosure are applied to a light-based microscope, theelectrons may be any other moving particle capable of being utilized togenerate images of a sample.

In operation, electron microscope 402 is housed in a high vacuumchamber. Electrons 406 are emitted by electron source 404, and, asemitted electrons 406 move from electron source 404 through electronmicroscope 402, the electrons are focused into one or more beams 410 andmaintained by first set of electrostatic and/or electromagnetic lenses408. The electrons in beam 410 encounter sample 412 and are eitherabsorbed by, scattered by or passed through sample 412. Becausedifferent regions of sample 412 are variously transparent to electrons,different amounts of electrons pass through these regions. Electronsthat have encountered sample 412 are collected by detector 416, which ineffect captures uncorrected image 418 of sample 412. Post-detectionimage processor 430 receives data of uncorrected image 418 and processesit to correct uncorrected image 418 for beam error parameters, includingbut not limited to defocus, astigmatism and spherical aberration.Post-detection image processor 430 outputs corrected image 432, which isuncorrected image 418 that has been corrected by post-detection imageprocessor 430 for beam error parameters, including but not limited todefocus, astigmatism and spherical aberration. As previously notedherein, cloud 50 may supplement, support or replace some or all of thefunctionality of post-detection processor 430 and corrected image 432.Additionally, some or all of the functionality of post-detectionprocessor 430 and corrected image 432 may be implemented as a node 10(shown in FIGS. 1 and 2) of cloud 50.

Turning now to a more detailed description of the principles ofoperation of one or more embodiments of the present disclosure, FIG. 5depicts a simplified diagram illustrating a plurality of electrons thathave been accelerated to travel in patterns, which are representeddiagrammatically as a plurality of rays 504A, 504B, 504C that form anelectron beam 410A. The electron beam 410A is focused to a point 502 ona transparent sample 412A. Three rays 504A, 504B, 504C are shown in FIG.5 for ease of illustration, however, it is understood that any number ofrays, as well as any pattern (other than rays) of accelerated electrontravel may be used without departing from the scope of the presentdisclosure. If electron beam 410A is perfectly focused on point 502, theelectrons that pass through point 502 of sample 412A may be collectedone pixel at a time by a raster scan method, thereby forming a highresolution image (not shown) of sample 412A.

FIG. 6A depicts a simplified diagram illustrating a slightly defocusedelectron beam 410B (having rays 504D, 504E, 540F) passing through asample 412B to a single integrating detector 416A. As shown in FIG. 6A,electron beam 410B is focused below sample 412B such that an area 602 ofsample 412B is illuminated by electron beam 410B. If electron beam 410Bis focused below sample 412B as shown in FIG. 6A, the electrons thatpass through sample 412B pass through area 602 of sample 412A. Thus, theinformation received at integrating detector 416A is averaged over area602, which causes a loss in resolution of an image (not shown) of sample412A formed at a detector (not shown) by a raster scan method fromdefocused electron beam 410B.

FIG. 6B depicts a simplified diagram illustrating a slightly defocusedbeam 410B (having rays 504D, 504E, 540F) passing through sample 412B toa position sensitive detector 416B. FIG. 6B shows the same geometry asFIG. 6A but uses position sensitive detector 416B located apredetermined distance behind sample 412B such that the spatialdistribution of the electrons may be collected in the plane of detector416B. FIG. 6B illustrates three different positions in the spatialdistribution, which are labeled in FIG. 6B as 1, 2, and 3. Position 1 iscentered on the axis of beam 410B (i.e., ray 504E), and it originatesfrom the center of illuminate area 602 on sample 412B. However, position2 originates from a position (i.e., ray 504F) on sample 412B that isshifted relative to the center of illuminated area 602. Position 3 alsooriginates from a position (i.e., ray 504D) on sample 412B that isshifted relative to the center of illuminated area 602 but in adifferent direction than position 2 and by a different amount.

Although FIG. 6B shows, for ease of illustration, the spatialdistribution in the plane of the sheet of paper on which the figure isdrawn, the illustrated principles apply in directions normal to theplane of the illustration. In the plane of detector 416B, electrons withcoordinates (X_(d), Y_(d)) are related to sample coordinates (X_(s),Y_(s)) according to the equation (X_(s), Y_(s))=(D/(D+L)) (X_(d),Y_(d)), wherein subscript “s” stands for sample, subscript “d” standsfor detector, and capital letter “D” shown as a non-subscript representsthe defocus distance “D” from point 502 of electron beam 410B to sample412B, and “L” is the distance from the sample to the detector. Thedefocus distance (D) can be either positive (i.e., electron beam 410B isfocused in front of the sample 412B) or negative (i.e., electron beam410B focused behind sample 412B).

The electron detector 416B acquires an N×M matrix of electronintensities, P(N,M), in the detector plane. Each detector pixel P(i,j),with 1<i<N and 1<j<M, corresponds to specific sample coordinates (X_(s),Y_(s)). Due to defocus, astigmatism and spherical aberration, therelation between the detector pixel P(i,j) and sample coordinates(X_(s), Y_(s)) is not a priori known, but depends on defocus,astigmatism and spherical aberration in a straightforward mathematicalmanner. Thus, when defocus, astigmatism and spherical aberration aredetermined, the pixel intensity P(i,j) can be assigned to thecorresponding sample position (X_(s), Y_(s)), and a corrected mapI(X_(s), Y_(s)) of the image intensities can be constructed in thesample plane. This mapping is done for every pixel in the detector. Ifthe position of the electron beam is changed relative to the sample(either by scanning the electron beam or by moving the sample), a newdetector intensity map P(N,M) can be acquired for this new position, anda new intensity map I(X_(s), Y_(s)) can be constructed for the newposition of the electron beam relative to the sample. The intensity mapsI (X_(s), Y_(s)) for different electron beam or sample positions arespatially added to obtain a real space image of the entire sample. Ifthe choice of defocus D is incorrect, the partial images added togetherwill not properly overlap into a combined image, i.e., the overlappedregions of the partial images will be out of focus. By varying thedefocus D, the combined image may be observed as a function of D, andthe defocus D for which the overlapped regions of the partial images arein focus be determined. Thus, the image can be focused using softwarealgorithms running on post-detection image processor 430 (shown in FIG.4) that implement the disclosed post-detection image correction systemsand methodologies after the dataset has been acquired. Focusing can beperformed by a person who observes the image on a computer screen andadjusts defocus D interactively. Focusing may also be performed by acomputer algorithm that uses a suitable numerical measure to determinethe optimum defocus D.

To define a spherical coordinate system, one must choose two orthogonaldirections (namely, the zenith and the azimuth reference) and an originpoint in space. These choices determine a reference plane that containsthe origin point and is perpendicular to the zenith. The sphericalcoordinates of a point are then defined as follows: the radius or radialdistance is the Euclidean distance from the origin to the point; theinclination (or polar angle) is the angle between the zenith directionand the line segment defined by the origin and the point; and the heazimuth (or azimuthal angle) is the signed angle measured from theazimuth reference direction to the orthogonal projection of the linesegment defined by the origin and the point on the reference plane.

In general, astigmatism occurs because the defocus distance D is notconstant with the azimuthal angle. As a point of comparison, in lightoptical systems astigmatism can usually be eliminated by a carefuldesign of the lens system. In electron microscopy, astigmatism is alwayspresent. A known configuration for addressing astigmatism is to providespecial optical elements (e.g., stigmators) that must be adjusted by amicroscope operator prior to image detection/acquisition. One or moreembodiments of the present disclosure take advantage of the observationthat, when astigmatism is present, it corresponds to a defocus D thatdepends on azimuth φ, d(φ). Thus, application of the disclosedpost-detection image correction systems and methodologies to correctboth astigmatism and defocus is not significantly more computationallydifficult than correcting defocus by itself.

An exemplary application of the disclosed post-detection imagecorrection systems and methodologies to correct both astigmatism anddefocus proceeds as follows. After acquiring the electron distributionsin the detector plane for an “N” by “M” matrix of pixel positions,P(N,M), on sample 412B, detector 416B now has acquired a defocused andastigmatic image (e.g., uncorrected image 418 shown in FIG. 4) of sample412B. Each detector pixel P(i,j), with 1<i<N and 1<j<M, corresponds tospecific sample coordinates (X_(s), Y_(s)). Due to defocus, astigmatismand spherical aberration, the relation between the detector pixel P(i,j)and sample coordinates (X_(s), Y_(s)) is not a priori known, but dependson defocus, astigmatism and spherical aberration in a straightforwardmathematical manner. Thus, when defocus, astigmatism and sphericalaberration are determined, the pixel intensity P(i,j) can be assigned tothe corresponding sample position (X_(s), Y_(s)), and a corrected mapI(X_(s), Y_(s)) of the image intensities can be constructed in thesample plane. This mapping is done for every pixel in the detector. Ifthe position of the electron beam is changed relative to the sample(either by scanning the electron beam or by moving the sample), a newdetector intensity map P(N,M) can be acquired for this new position, anda new intensity map I(X_(s), Y_(s)) can be constructed for the newposition of the electron beam relative to the sample. The intensity mapsI (X_(s), Y_(s)) for different electron beam or sample positions arespatially added to obtain a real space image of the entire sample.Post-detection image processor 430 (shown in FIG. 4) now adjusts thedefocused and astigmatic image. If the choice of defocus D andastigmatism d(φ) are incorrect, the partial images added together willnot properly overlap into a combined image, i.e. the overlapped regionsof the partial images will be out of focus and not properly stigmated.By varying the defocus D and astigmatism d(φ), the image can be observedas a function of defocus and d(φ), and the defocus and the azimuthallyvarying defocus d(φ) for which the image is in focus and stigmated canbe determined. Thus, the image can be focused and stigmated usingsoftware algorithms running on post-detection image processor 430 (shownin FIG. 4) that implement the disclosed post-detection image correctionsystems and methodologies after the dataset has been acquired. Focusingand stigmating can be performed by a person who observes the image on acomputer screen and adjusts defocus D and astigmatism d(φ)interactively. Focusing may also be performed by a computer algorithmthat uses a suitable numerical measure to determine the optimum defocusD and astigmatism d(φ).

FIG. 7A depicts a simplified diagram illustrating the focusing of anelectron beam 410C without spherical aberration, and FIG. 7B depicts asimplified diagram illustrating the focusing of an electron beam 410Dwith spherical aberration. In general, spherical aberration is anoptical effect observed in an optical device (e.g., a lens, a mirror,etc.) that occurs due to the increased refraction of light rays whenthey strike a lens near its edge, in comparison with those that strikenearer the center. The increased refraction occurs when rays furtheraway from the optical axis are focused too strongly. Thus, sphericalaberration results in an imperfection of the produced image. All lensessuffer from spherical aberration. As shown in FIG. 7B, the intensitydistribution of electron beam 410D at the sample position is blurred dueto spherical aberration. The displacement of electron rays 504G, 504H,504I, 504J, 504K at the sample due to spherical aberration is given byC_(s)·θ³. If it is assumed that a given electron ray has angles (θ_(x),θ_(y)) relative to the optical axis, the ray will intersect sample 412Cat position (X_(s), Y_(s))=(C_(s)·θ_(x) ³, C_(s)·θ_(y) ³). In thedetector plane, the position of this same ray is given by (X_(d),Y_(d))=(X_(s)+L·θ_(x), Y_(s)+L·θ_(y)). It is noted that, in the presentexample, the small angle approximation is used, wherein tan(θ)=sin(θ)=0.

An exemplary application of the disclosed post-detection imagecorrection systems and methodologies to correct spherical aberration andastigmatism and defocus proceeds as follows. After acquiring theelectron distributions in the detector plane for an “N” by “M” matrix ofpixel positions, P(N,M) on sample 412B, detector 416B now has acquired adefocused and astigmatic image (e.g., uncorrected image 418 shown inFIG. 4) of sample 412B. Each detector pixel P(i,j), with 1<i<N and1<j<M, corresponds to specific sample coordinates (X_(s), Y_(s)). Due todefocus, astigmatism and spherical aberration, the relation between thedetector pixel P(i,j) and sample coordinates (X_(s), Y_(s)) is not apriori known, but depends on defocus, astigmatism and sphericalaberration in a straightforward mathematical manner. Thus, when defocus,astigmatism and spherical aberration are determined, the pixel intensityP(i,j) can be assigned to the corresponding sample position (X_(s),Y_(s)), and a corrected map I(X_(s), Y_(s)) of the image intensities canbe constructed in the sample plane. This mapping is done for every pixelin the detector. If the position of the electron beam is changedrelative to the sample (either by scanning the electron beam or bymoving the sample), a new detector intensity map P(N,M) can be acquiredfor this new position, and a new intensity map I(X_(s), Y_(s)) can beconstructed for the new position of the electron beam relative to thesample. The intensity maps I (X_(s), Y_(s)) for different electron beamor sample positions are spatially added to obtain a real space image ofthe entire sample. Post-detection image processor 430 (shown in FIG. 4)now adjusts the uncorrected image which includes defocus, astigmatism,and spherical aberration. If the choice of spherical aberration,defocus, and astigmatism is incorrect, the partial images added togetherwill not properly overlap into a combined image, i.e. the overlappedregions of the partial images will be out of focus, astigmatic, andradially distorted. By varying the spherical aberration coefficientC_(s), defocus, and astigmatism the image can observed as a function ofC_(s), defocus, and astigmatism and the values of C_(s), defocus, andastigmatism for which the image is sharpest can be determined. Thus, thespherical aberration coefficient C_(s), defocus, and astigmatism inuncorrected image 418 (shown in FIG. 4) can be corrected using softwarealgorithms running on post-detection image processor 430 (shown in FIG.4) that implement the disclosed post-detection image correction systemsand methodologies after the dataset has been acquired. Correction ofC_(s), defocus, and astigmatism can be performed by a person whoobserves the image on a computer screen and adjusts C_(s), defocus, andastigmatism interactively. Correction of C_(s), defocus, and astigmatismcan also be performed by a computer algorithm that uses a suitablenumerical measure to determine the value of Cs, defocus, andastigmatism.

FIG. 8 depicts a simplified diagram illustrating a slightly defocusedelectron beam 410E using a position sensitive detector 416C, wherein aspatial electron distribution (i.e., virtual detector 802) is positionedbetween sample 412D and integrating detector 416C according to one ormore embodiments. Detector 416C does not have to be located immediatelybehind sample 412D. The plane in which it is desirable to observe thespatial electron distribution (i.e., virtual detector 802) can betransferred to the physical location of the detector plane (realdetector 416C) by a suitable electrostatic or magnetic transfer lens414A, or a plurality of such lenses as may be suitable. Themagnification from virtual detector 802 to real detector 416C need notbe equal to one, but can for example, be greater than one as shown inFIG. 8.

Defocus, astigmatism, and spherical aberration do not need to beconstant during detection of the image. In particular, it is notuncommon for defocus and astigmatism to drift during a lengthy datadetection, or the sample itself may not be perfectly flat such thatdefocus changes as a function of sample position. Accordingly,post-detection image processor 430 may include functionality that allowsthese variables to be functions of time, as well as position (X_(s),Y_(s)), in order to obtain a corrected image with optimum resolutionacross the full field of view. While taking into account time can makeimage correction more complex, suitable algorithms may be designed thatincorporate such time and position-dependent corrections. Although suchtime and position-dependent corrections can be implemented interactivelyby a person, use of a suitable computer algorithm that monitors theimage resolution as a function of place and time may also beimplemented.

FIG. 9 depicts a simplified diagram illustrating a configurationaccording to one or more embodiments, wherein electron beam 410 (shownin FIG. 4) is implemented as multiple electron beams (1, 2, 3, . . . i).The different electron beams (1 to i) are shown with different defocusvalues D_(i). A large position sensitive detector 416D (for instance asuitable 5 k×5 k imaging electron detector) detects the distributions ofthe electron intensities for each of the electron beams (1, 2, 3, . . .i) in both the X direction and the Y direction. Each electron beamilluminates a roughly circular area on imaging detector 416D. Care istaken to ensure that the intensity distributions of neighboring electronbeams do not overlap, yet are sufficiently close to make optimum use ofthe imaging detector. After detection of the multi-beam intensitydistributions, all electrons beams (1, 2, 3, . . . i) are advanced to anext position on sample 412E, either by scanning all the electron beamsin unison, or by translating sample 412E. In this manner, an “N” by “M”(N×M) matrix data set is acquired for each electron beam to form an N×Mimage for each electron beam. If there are a total of U×V electron beams(U in the X-direction, V in the Y-direction) a total of U×V detectorimages are acquired, wherein each detector image includes an N×Mintensity distribution. For each electron beam (1, 2, 3, . . . i),defocus, astigmatism, and spherical aberration in an uncorrected image(e.g., uncorrected image 418 shown in FIG. 4) formed from the electronbeams is corrected post-detection by post-detection image processor 430(shown in FIG. 4). After correction of each of the U×V sub-images (eachformed by one electron beam) the final image is assembled from thecorrected images.

FIG. 10 depicts equations that may be used in reconstruction of theobject plane (i.e., correcting an uncorrected, acquired image fordefocus, astigmatism and spherical aberration) according to one or moreembodiments of the present disclosure. It is assumed that an electronray makes an angle with the Z-axis (with the electron beam traveling inthe positive Z direction). In general, ray displacements in the Xdirection and the Y direction with angle (σ_(x), σ_(y)), compared to theray with zero angles, are given by Equation (1), wherein C₁₀ is defocus,C_(12a) and C_(12b) are astigmatism (in two planes rotated by 45degrees), and C₃₀ is spherical aberration (also denoted as C_(s)herein). Equation (1) is a known in the electron microscopy art. In thedetector plane, wherein d is defocus and l is a distance from the sampleto the detector, Equation (2) is utilized. In the object (sample) plane,Equation (3) is utilized.

For a given object, and for constants d, l, C_(12a), C_(12b), C₃₀=C_(s),a pixel P(i,j) is selected from the uncorrected detector intensity mapP(N,M) with 1<i<N and 1<j<M. For this pixel P(i,j) the detector position(S_(x) ^(D),S_(y) ^(D)) is known from the detector pixel geometry. Now,using Equation (2) with initial estimated values for defocus d,astigmatism parameters C_(12a) and C_(12b), and spherical aberrationC₃₀=C_(s), θ_(x) and θ_(y) can be solved. Next, the initial values ford, C_(12a) and C_(12b), C₃₀ ⁼C_(s), and θ_(x) and θ_(y) are entered intoEquation (3), and the sample coordinates)(S_(x) ^(O),S_(y) ^(O))corresponding to detector pixel element P(i,j) are obtained. To takeinto account finite source size, small random offsets (δ_(x) ^(s),δ_(y)^(s)) to (S_(x) ^(O),S_(y) ^(O)) and (S_(x) ^(D),S_(y) ^(D)) may beadded according to the lateral extent of the de-magnified source (forexample, Gaussian). For each detector pixel, the average value of (S_(x)^(D), S_(y) ^(D)) is known, with an accuracy limited by the size of thepixel. The intensity of the ray (as read from the detector, i.e. thevalue of the matrix pixel element P(i,j)) is then assigned to the objectplane at coordinates (S_(x) ^(O),S_(y) ^(O)). This process is repeatedfor each pixel P(i,j) with 1<i<N and 1<j<M within the P(N,M) matrix. Theback-transformed intensity distribution in the object plane for theestimated values of d, C_(12a), C_(12b), C₃₀=C_(s) is now known. Thisprocedure is repeated for the next beam position until all beampositions have been covered. All back-transformed intensitydistributions in the object plane are added to obtain theback-transformed image.

With overlap between consecutive beam positions, the reconstructedobject planes must give identical results in the overlapped regions,provided that the correct values for d, C_(12a), C_(12b), C₃₀=C_(s) havebeen estimated. If these estimated values are incorrect, there willstill be defects and distortions in the reconstructed object plane, anda new estimate must be entered. The distortions in the reconstructedobject plane can then be iteratively reduced until the differences inthe overlapped regions are minimized. In a multi-beam configuration ofthe present disclosure, this process is applied to each of the beams.

FIG. 11 depicts a flow diagram illustrating a methodology 1100 accordingto one or more embodiments. In general, the electron beam will not beexactly focused in the sample plane. In addition, there will beastigmatism and spherical aberration present. Methodology 1100 may beapplied to correct all three defects (defocus, astigmatism, andspherical aberration) after detection of the data that forms theuncorrected image. Methodology 1100 begins at block 1102 by forming aspatial intensity map of the sample coordinates for a given choice ofdefocus, astigmatism d(φ) and spherical aberration coefficient C_(s). Atblock 1104 the intensity maps are spatially added to obtain anuncorrected image of the sample having real-space and angular-spaceinformation. If the choice of defocus, astigmatism d(φ) and sphericalaberration coefficient C_(s), is incorrect, the image will be blurred.By varying defocus and astigmatism d(φ) and the spherical aberrationcoefficient C_(s), the image can be observed as a function of d(φ) andC_(s), and the values of d(φ) and C_(s) for which the image is sharpestmay be determined. Thus, block 1106 utilizes real-space andangular-space information of the detected, uncorrected image to developa corrected image of the sample based at least in part on processing thereal-space and angular-space information of the detected, uncorrectedimage. Correction of defocus, astigmatism d(φ) and spherical aberrationcoefficient C_(s) can be performed by a person who observes the image ona computer screen and adjusts defocus, astigmatism d(φ) and sphericalaberration coefficient C_(s) interactively. Correction of defocus,astigmatism d(φ) and spherical aberration coefficient C_(s) can beperformed by a computer algorithm that uses a suitable numerical measureto determine the values of defocus and astigmatism d(φ) and sphericalaberration coefficient C_(s).

Thus, it can be seen from the foregoing description and illustrationsthat one or more embodiments of the present disclosure provide technicalfeatures and benefits. The systems and methodologies of the presentdisclosure may be particularly suitable for, but not limited to,acquiring images with spatial resolution as small as a few nanometers ofbiological tissue samples or other nano-structured organic or inorganicsamples, including samples of semiconductor circuits. The systems andmethodologies of the present disclosure may also be applied tolight-microscopy, wherein the optical system contains significantdefocus and/or astigmatism and/or spherical aberration. Because thesystem acquires, for each pixel in the image, a 2D image of the detectorintensity distribution, very large datasets are acquired. These datasetsmay be stored on the detection computer system, wherein thepost-detection corrections may also be performed. Alternatively, thedatasets may be transferred to another computer system or data server,and the post-detection corrections may be performed later, possibly at adifferent location, and a different computing system. The correction maybe performed in a cloud computing system (e.g., cloud 50 shown in FIGS.2 and 4), and the corrected image may be returned to the originalcreator of the image, or may be stored in the cloud computing system, ormay be delivered to another party. Large numbers of corrected images maybe combined to form 3D composite images of complex 3D structure, such as(but not limited to) nano-scale materials and devices, or biologicaltissues.

Additionally, the use of multiple parallel beams in the presentdisclosure greatly reduces total image detection/acquisition time. Forexample, if a 16×16 beam array (256 electron beams) is used, thedetection time would reduce by a factor 256, provided that the electroncurrent in each electron beam is the same as the electron current thatcan be realized in a single electron beam. If the desired field of viewis 1×1 millimeter², the electron beams would be spaced by 1000/16=62.5micrometers in both the X direction and the Y direction. The electronbeams can be packed in various manners, including but not limited tosquare or hexagonal arrays. A multi-beam electron source can befabricated on a monolithic chip using standard IC (integrated circuit)and MEMS (micro-electromechanical system) micro-fabrication techniques.The electron source to sample distance may be kept at a minimum (e.g.,1-5 cm) without beam crossovers between source and sample to minimizespace charge effects. However, other embodiments are also possible andare not precluded herein. Following the teachings of the presentdisclosure, the electron beams may be, but do not have to be focused orstigmated individually. Defocus and astigmatism for each beam arecorrected after data detection is complete. With an 8 k×8 k imagingdetector, electron beam arrays up to 32×32 (with about 250×250 pixelsavailable for each beam to measure the intensity distribution in thedetector plane) are feasible. Accordingly, the operation of a computersystem implementing one or more of the disclosed embodiments can beimproved.

Referring now to FIG. 12, a computer program product 1200 in accordancewith an embodiment that includes a computer readable storage medium 1202and program instructions 1204 is generally shown.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentdisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

It will be understood that those skilled in the art, both now and in thefuture, may make various improvements and enhancements which fall withinthe scope of the claims which follow.

What is claimed is:
 1. A computer-implemented method of generating imagedata of a sample, the method comprising: receiving, using a detectorsystem, particles from the sample; based at least in part on receivingthe particles, generating, using the detector system, uncorrected imagedata corresponding to the particles, wherein at least a portion of theuncorrected image data represents image error of an image of the sample;and generating, using a post-detection image processor, corrected imagedata by processing the uncorrected image data to remove the effects ofthe portion of the uncorrected image data that represents the imageerror of the image.
 2. The computer-implemented method of claim 1,wherein: the particles comprise electrons; the electrons comprise aplurality of electron beams; and the plurality of electron beams aresubstantially parallel with respect to one another.
 3. Thecomputer-implemented method of claim 1, wherein the image error of theimage comprises at least one of: defocus; astigmatism; and sphericalaberration.
 4. The computer-implemented method of claim 1 furthercomprising: transferring, by a lens system, a plane of a spatialdistribution of the particles to the detector system.
 5. Thecomputer-implemented method of claim 1, wherein the image error of theimage comprises defocus, astigmatism, and spherical aberration.
 6. Thecomputer-implemented method of claim 1, wherein processing theuncorrected image data to remove the effects of the portion of theuncorrected image data that represents the image error of the imagecomprises: determining a defocus of the sample; and utilizing thedetermined defocus of the sample to determine relationships between theuncorrected image data and coordinates of the sample.
 7. Thecomputer-implemented method of claim 6, wherein processing theuncorrected image data to remove the effects of the portion of theuncorrected image data that represents the image error of the imagefurther comprises: utilizing the determined relationships between theuncorrected image data and coordinates of the sample to generatecorrected image data that represent intensities of the particles.
 8. Thecomputer-implemented method of claim 7, wherein processing theuncorrected image data to remove the effects of the portion of theuncorrected image data that represents the image error of the imagefurther comprises: assigning coordinates of the sample to the correctedimage data that represent the intensities of the particles.
 9. Thecomputer-implemented method of claim 8, wherein processing theuncorrected image data to remove the effects of the portion of theuncorrected image data that represents the image error of the imagefurther comprises: determining another defocus of the sample; utilizingthe determined another defocus of the sample to determine another set ofrelationships between the uncorrected image data and coordinates of thesample; utilizing the determined another set of relationships betweenthe uncorrected image data and coordinates of the sample to generateanother set of corrected image data that represent the intensities ofthe particles; and assigning coordinates of the sample to the anotherset of corrected image data that represent the intensities of theparticles.
 10. A computer-implemented method of generating image data ofa sample, the method comprising: receiving, using a two-dimensionalcollection surface of a detector, particles from the sample; based atleast in part on receiving the particles, generating, using thedetector, uncorrected image data corresponding to the particles, whereinat least a portion of the uncorrected image data represents defocus,astigmatism or spherical aberration of an image of the sample; storing,using a memory, the uncorrected image data, which includes storing theportion of the uncorrected image data that represents defocus,astigmatism or spherical aberration of the image of the sample;accessing, using a post-detection image processor, the storeduncorrected image data, which includes accessing the stored portion ofthe uncorrected image data that represents defocus, astigmatism orspherical aberration of the image of the sample; and generating, usingthe post-detection image processor, corrected image data by processingthe uncorrected image data to remove the effects of the portion of theuncorrected image data that represents defocus, astigmatism or sphericalaberration of the image.
 11. The computer-implemented method of claim10, wherein the detector system is configured to generate theuncorrected image data by: measuring intensities of the receivedparticles; and generating data that represent the intensities of thereceived particles, wherein the uncorrected image data comprises thedata that represent the intensities of the received particles.
 12. Thecomputer-implemented method of claim 11, wherein processing theuncorrected image data to remove the effects of the portion of theuncorrected image data that represents defocus, astigmatism or sphericalaberration of the image comprises: determining a defocus, astigmatism orspherical aberration of the sample; and utilizing the determineddefocus, astigmatism or spherical aberration of the sample to determinerelationships between the data that represent the intensities of thereceived particles and coordinates of the sample.
 13. Thecomputer-implemented method of claim 12, wherein processing theuncorrected image data to remove the effects of the portion of theuncorrected image data that represents defocus, astigmatism or sphericalaberration of the image further comprises: utilizing the determinedrelationships between the data that represent the intensities of thereceived particles and coordinates of the sample to generate correcteddata that represent the intensities of the received particles.
 14. Thecomputer-implemented method of claim 13, wherein processing theuncorrected image data to remove the effects of the portion of theuncorrected image data that represents defocus, astigmatism or sphericalaberration of the image further comprises: assigning coordinates of thesample to the corrected data that represent the intensities of thereceived particles.
 15. The computer-implemented method of claim 14,wherein processing the uncorrected image data to remove the effects ofthe portion of the uncorrected image data that represents defocus,astigmatism or spherical aberration of the image further comprises:determining another defocus, astigmatism or spherical aberration of thesample; utilizing the determined another defocus, astigmatism orspherical aberration of the sample to determine another set ofrelationships between the data that represent the intensities of thereceived particles and coordinates of the sample; utilizing thedetermined another set of relationships between the data that representthe intensities of the received particles and coordinates of the sampleto generate another set of corrected data that represent the intensitiesof the received particles; and assigning coordinates of the sample tothe another set of corrected data that represent the intensities of thereceived particles.